| 1. |
- Arheimer, Berit, et al.
(author)
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The IAHS Science for Solutions decade, with Hydrology Engaging Local People IN a Global world (HELPING)
- 2024
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In: Hydrological Sciences Journal. - 0262-6667 .- 2150-3435.
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Journal article (peer-reviewed)abstract
- The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions - may it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes.
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| 2. |
- Van Vliet, Michelle T. H., et al.
(author)
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Global river water quality under climate change and hydroclimatic extremes
- 2023
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In: Nature Reviews Earth & Environment. - 2662-138X. ; 4, s. 687-702
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Research review (peer-reviewed)abstract
- Climate change and extreme weather events (such as droughts, heatwaves, rainstorms and floods) pose serious challenges for water management, in terms of both water resources availability and water quality. However, the responses and mechanisms of river water quality under more frequent and intense hydroclimatic extremes are not well understood. In this Review, we assess the impacts of hydroclimatic extremes and multidecadal climate change on a wide range of water quality constituents to identify the key responses and driving mechanisms. Comparison of 965 case studies indicates that river water quality generally deteriorates under droughts and heatwaves (68% of compiled cases), rainstorms and floods (51%) and under long-term climate change (56%). Also improvements or mixed responses are reported owing to counteracting mechanisms, for example, increased pollutant mobilization versus dilution during flood events. River water quality responses under multidecadal climate change are driven by hydrological alterations, rises in water and soil temperatures and interactions among hydroclimatic, land use and human drivers. These complex interactions synergistically influence the sources, transport and transformation of all water quality constituents. Future research must target tools, techniques and models that support the design of robust water quality management strategies, in a world that is facing more frequent and severe hydroclimatic extremes.
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| 3. |
- Ebrahim, Girma Yimer, et al.
(author)
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Downscaling technique uncertainty in assessing hydrological impact of climate change in the Upper Beles River Basin, Ethiopia
- 2013
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In: Hydrology Research. - : IWA Publishing. - 1998-9563 .- 0029-1277 .- 2224-7955. ; 44:2, s. 377-398
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Journal article (peer-reviewed)abstract
- We investigate the uncertainty associated with downscaling techniques in climate impact studies, using the Upper Beles River Basin (Upper Blue Nile) in Ethiopia as an example. The main aim of the study is to estimate the two sources of uncertainty in downscaling models: (1) epistemic uncertainty and (2) stochastic uncertainty due to inherent variability. The first aim was achieved by driving a Hydrologic Engineering Centre-Hydrological Modelling System (HEC-HMS) model with downscaled daily precipitation and temperature using three downscaling models: Statistical Downscaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) and an artificial neural network (ANN). The second objective was achieved by driving the hydrological model with individual downscaled daily precipitation and temperature ensemble members, generated by using the stochastic component of the SDSM. Results of the study showed that the downscaled precipitation and temperature time series are sensitive to the downscaling techniques. More specifically, the percentage change in mean annual flow ranges from 5% reduction to 18% increase. By analyzing the uncertainty of the SDSM model ensembles, it was found that the percentage change in mean annual flow ranges from 6% increase to 8% decrease. This study demonstrates the need for extreme caution in interpreting and using the output of a single downscaling model.
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